Mathematics For Machine Learning, M. P. Deisenroth, A. A. Faisal & C. S. Ong, Cambridge University Press

Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning.

C# programmers: no more translating data structures from C++ or Java to use in your programs! Mike McMillan provides a tutorial on how to use data structures and algorithms plus the first comprehensive reference for C# implementation of data structures and algorithms found in the .NET Framework library, as well as those developed by the programmer. The approach is very practical, using timing tests rather than Big O notation to analyze the efficiency of an approach. Coverage includes arrays and array lists, linked lists, hash tables, dictionaries, trees, graphs, and sorting and searching algorithms, as well as more advanced algorithms such as probabilistic algorithms and dynamic programming. This is the perfect resource for C# professionals and students alike.

Focusing on discrete models across a variety of biological subdisciplines, this introductory textbook includes linear and non-linear models of populations, Markov models of molecular evolution, phylogenetic tree construction from DNA sequence data, genetics, and infectious disease models. Assuming no knowledge of calculus, the development of mathematical topics, such as matrix algebra and basic probability, is motivated by the biological models.

A master class to learn convex optimization for ML and its applications to different fields and areas of engineering

Use Scikit, learn NumPy, Pandas, Matplotlib, Seaborn and dive into machine learning A-Z with Python and Data Science.